I. General Information

1. Course Title:
Introduction to Statistics

2. Course Prefix & Number:
MATH 1460

3. Course Credits and Contact Hours:

Credits: 4

Lecture Hours: 4

Lab Hours: 0

4. Course Description:

This course covers descriptive statistics, sampling, probability, probability distributions, normal probability distributions, estimates and sample sizes, hypothesis testing, correlation and regression, inferences of two samples, and process control.

5. Placement Tests Required:

6. Prerequisite Courses:

7. Other Prerequisites

Grade of “C” or higher in prerequisite course.

9. Co-requisite Courses:

II. Transfer and Articulation

1. Course Equivalency - similar course from other regional institutions:

3. Prior Learning - the following prior learning methods are acceptable for this course:
Advanced Placement (AP)

III. Course Purpose

2. MN Transfer Curriculum (General Education) Courses - This course fulfills the following goal area(s) of the MN Transfer Curriculum:
Goal 4 – Mathematical/Logical Reasoning

IV. Learning Outcomes

1. College-Wide Outcomes

2. Course Specific Outcomes - Students will be able to achieve the following measurable goals upon completion of the course:

V. Topical Outline

Listed below are major areas of content typically covered in this course.

1. Lecture Sessions

1. Introduction to Statistics
• Define types of data
• Use critical thinking skills
• Describe methods of collecting data
2. Summarizing and Graphing Data
• Construct graphical representations of data and estimate common numerical measures from them
• Analyze misuse of graphs for data
3. Describe and compare data in statistical terms
• Compute measures of center, z-scores, variation, quartiles, and percentile ranks from data and give interpretations of these numerical measures
• Develop boxplot representations and interpret distribution characteristics
4. Probability
• Calculate basic probabilities
• Addition rule
• Multiplication rule
• Counting
5. Discrete Probability Distributions
• Use binomial distributions to determine characteristics of data
6. Normal Probability Distributions
• Apply normal approximation to estimate projected outcomes and percentiles for data that is normally distributed
7. Estimates and Sample Sizes
• Compute and interpret confidence intervals and sample sizes for means, proportions, and variances
8. Hypothesis Testing
• Perform hypothesis testing for claims about proportions, means, and variations and interpret the results of these tests
9. Inferences from two samples
• Develop inferences about two proportions, two means and dependent samples.
10. Correlation
• Compute and interpret the correlation coefficient as a measure of the strength of the linear association between two numeric values
11. Linear Regression
• Apply regression methods to estimate dependent variable values
• Interpret slope and constant in regression equations.
12. Goodness-of-fit and Contingency Tables
• Determine correlations conducting goodness-of-fit test
13. Analysis of Variance